Field of the Invention
The present invention relates to the oil industry, and more particularly, the exploitation of underground reservoirs, such as oil reservoirs or gas storage sites.
Description of the Prior Art
The optimization and the exploitation of an oil deposit relies on a description that is as accurate as possible of the structure, of the petrophysical properties, of the fluid properties, etc., of the deposit being studied. For this, the experts use a software tool which makes it possible to take account of these aspects in an approximate manner known as the reservoir model. Such a model constitutes a mock-up of the subsoil, representative both of its structure and of its behavior. Generally, this type of mock-up is represented on a computer by execution of a computer program which is then called a numerical model. A reservoir model comprises a meshing or grid, generally three-dimensional, associated with one or more maps of petrophysical properties (facies, porosity, permeability, saturation, etc.). The association assigns values of these petrophysical properties to each of the meshes of the grid.
These models, which are well known and widely used in the oil industry, make it possible to determine numerous technical parameters relating to the study or exploitation of a reservoir, of hydrocarbons for example. In practice, when the reservoir model is representative of the structure of the reservoir and of its behavior, the engineer uses it for example to determine the areas which have the greatest chances of containing hydrocarbons, the areas in which it may be interesting/necessary to drill an injection or production well to improve the recovery of the hydrocarbons, the type of tools to be used, the properties of the fluids used and recovered, and so on. These interpretations of reservoir models in terms of “exploitation technical parameters” are well known. Similarly, the modeling of the CO2 storage sites makes it possible to monitor these sites, to detect unexpected behaviors and to predict the movement of the injected CO2.
The function of a reservoir model is therefore to best account for all the information known concerning a reservoir. A reservoir model is representative when a reservoir simulation supplies numerical responses that are very close to the history data already observed. History data is the term used to designate the production data obtained from measurements on the wells in response to the production from the reservoir (production of oil, production of water from one or more wells, breakthrough time, gas/oil ratio (GOR), proportion of production water (“water cut”), and/or the repetitive seismic data (4D seismic impedances in one or more regions, etc.)).
A reservoir simulation is a technique that makes it possible to simulate the fluid flows within a reservoir by software executed on a computer known as a “flow simulator”, and of the reservoir model. For example, the PumaFlow® (IFP Energies nouvelles, France) and ECLIPSE® (Schlumberger, United States) software packages are flow simulators.
For this, the integration of all the available data is essential. These data generally comprise:                measurements at certain points of the geological formation of the property modeled, for example in wells. These data are called static because they do not vary over time (on the timescale of the production from the reservoir) and are directly linked to the property of interest.        “history data”, comprising production data, for example the fluid flow rates measured on the wells, the concentrations of tracers and data obtained from seismic acquisition campaigns repeated at successive times. These data are called dynamic because they change during exploitation and are indirectly linked to the properties assigned to the meshes of the reservoir model.        
The available static data are used to define random functions for each petrophysical property such as porosity or permeability. A representation of the spatial distribution of a petrophysical property is a realization of a random function. Generally, a realization is generated from a germ or a series of random numbers. Numerous simulation techniques exist, such as the Gaussian sequential simulation method, the Cholesky method or even the FFT-MA (fast Fourier transform with moving average) method. The following documents describe such methods:                Goovaerts, P., 1997, Geostatistics for Natural Resources Evaluation, Oxford Press, New York.        Le Ravalec M., Ncetinger B., and Hu L.-Y., 2000, The FFT Moving Average (FFT-MA) Generator: An Efficient Numerical Method for Generating and Conditioning Gaussian Simulations, Mathematical Geology, 32(6), 701-723.        
The techniques for integrating the dynamic data (production and/or 4D seismic) in a reservoir model are well known and are so-called “history matching” techniques. History matching modifies the parameters of a reservoir model, such as the permeabilities, the porosities or the skins of wells (representing damage around the well), the fault lines, etc., to minimize the differences between the measured history data and the corresponding responses simulated on the basis of the model by using a flow simulator. The parameters can be linked to geographic regions, like the permeabilities or porosities around one or more wells. The difference between real data and simulated responses forms a functional called an “objective function.” The history matching problem is resolved by minimizing this functional. Reservoir model perturbation techniques make it possible to modify a realization of a random function while ensuring the fact that the perturbed realization is also a realization of this same random function. Perturbation techniques that can be cited include the pilot points method developed by RamaRao et al. (1995) and Gomez-Hernandez et al. (1997), the gradual deformations method proposed by Hu (2000) and the probability perturbation method introduced by Caers (2003). These methods make it possible to modify the spatial distribution of the heterogeneities:                RamaRao, B. S., Lavenue, A. M., Marsilly, G. de, Marietta, M. G., 1995, Pilot Point Methodology for Automated Calibration of an Ensemble of Conditionally Simulated Transmissivity Fields. 1. Theory and Computational Experiments. WRR, 31(3), 475-493.        Gomez-Hernandez, J., Sahuquillo, A., and Capilla, J. E., 1997, Stochastic Simulation of Transmissivity Fields Conditional to Both Transmissivity and Piezometric Data, 1. Theory, J. of Hydrology, 203, 162-174.        Hu, L-Y., 2000, Gradual Deformation and Iterative Calibration of Gaussian—Related Stochastic Models, Math. Geol., 32(1), 87-108.        Caers, J., 2003, Geostatistical History Matching Under Training-Image Based Geological Constraints. SPE J. 8(3), 218-226.        
The determination of a matched reservoir model that observes the production data is very adversely affected by the computation times required. In practice, the matching process involves a very large number of iterations. For each of them, the model is given as input to a flow simulator and the computed responses are compared to the available production data. Now, the computation time for a simulation is generally of the order of a few hours. Consequently, the experts seek to limit the number of history matching iterations to limit the computation time and cost (simulations) while obtaining a reservoir model that is representative of the geological reservoir.